Title: A hybrid GA-ant colony approach for exploring the relationship between IT and firm performance

Authors: A. Azadeh, A. Keramati, H. Panahi

Addresses: Department of Industrial Engineering, Center of Excellence for Intelligent Experimental Mechanics; Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran. ' Department of Industrial Engineering, Center of Excellence for Intelligent Experimental Mechanics; Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran. ' Department of Industrial Engineering, Center of Excellence for Intelligent Experimental Mechanics; Department of Engineering Optimization Research, College of Engineering, University of Tehran, Iran

Abstract: Several studies were conducted during recent years on exploring the impact of Information Technology (IT) on the performance of the organisation. It is quite important to find a robust technique to identify the relationship between IT and organisational performance. A hybrid Genetic Algorithm (GA) Ant Colony Optimisation (ACO) approach is proposed for data clustering. This is because of the need for the application of metaheurisitic algorithms parallel to deterministic approaches. This study discusses and analyses data from 90 companies in a unique supply chain. The data includes 26 indices about IT and 11 indices about performance. The companies are classified with respect to the IT and performance indices (indicators). Then, IT clusters and performance clusters are mapped to one another and, consequently, the relationship between them is explored. In general, the result shows that there is a linear relationship between the IT status and performance of the companies, with few exceptions. This is the first study which integrates ant colony approach and GA for exploring the relationship between IT and firm performance.

Keywords: information technology; firm performance; cluster analysis; ant colony optimisation; ACO; genetic algorithms; GAs; hybrid; data clustering; metaheurisitics.

DOI: 10.1504/IJBIS.2009.025206

International Journal of Business Information Systems, 2009 Vol.4 No.5, pp.542 - 563

Available online: 16 May 2009 *

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